Many applications in digital imaging require the identification of objects in a digital image or video. For example, in the motion picture industry, it may be desirable to identify objects (e.g., actors or props) in an individual frame of a motion picture, and then to manipulate the pixel values of the identified main object to apply a desired special effect. The main object identification process is generally accomplished manually and typically requires manually outlining the object(s) of interest using a graphic user interface on a computer terminal. The image pixels for the identified objects are then modified by changing the objects with respect to the background in some predetermined manner. Alternately, it may be desirable to apply image modifications to the background. For example, in recent motion pictures the effects of blurring the background or changing the background to black and white have been used.
This manual object identification process is very labor intensive and hence costly to implement. Yet, the effect is so desirable that motion picture producers are willing to invest the expense to produce images having desired special effects. Of course, special effects are also desirable for use in still photograph by amateur or professional photographers. Such use is similarly limited by the cost and inconvenience of a manual object identification technique.
In the case where amateur photographers desire to apply special effects to still digital images, there is not only the manual labor required to manipulate the digital image, but also the effort needed to learn to use software that is capable of doing the manual object selection. If such image manipulation is not done regularly, the user has a certain amount of re-learning to do each time they desire to manipulate an image.
There are many references that describe techniques for identifying main objects in digital images. For example, a method for determining main objects in a photograph is described in U.S. Pat. No. 6,282,317, and methods for emphasis of main objects is described in U.S. Pat. Nos. 7,333,654 and 7,212,668. However, they only use information from two-dimensional digital images. Furthermore, their results are highly sensitive to image noise.
Consequently, a need exists in the art for an automated method of processing a digital image having reliably identified main objects in order to enable various image processing operations.